Legacy code slows your agents down.
We remove the drag.
Agents inherit your codebase's problems. Undocumented services, dead code, and tangled dependencies cut agent speed and accuracy the same way they slow your engineers. We modernize the code so both your people and your AI systems move faster, and we measure the difference.
Your AI coding tools are not slow.
Your codebase is.
Teams buy AI coding tools and get a fraction of the promised lift. The tools are not the problem. Agents reason over what is in the repo, and when the repo is undocumented services, dead code, and dependencies nobody untangled, they guess, invent structure that is not there, and produce changes engineers do not trust.
Modernization used to be a cost you could defer. Now it decides whether AI tooling compounds or stalls, and whether your team can carry the systems you want to own. The drag on agents is the same drag your engineers have felt for years. It finally has a price you can measure.
A codebase both your engineers
and your agents can read.
We rank modernization targets by how much they slow agents down, work through them, and prove the lift with before and after numbers.
Agent-impact modernization
Not a rewrite. We find the parts of the codebase that cost agents and engineers the most, including undocumented services, tangled dependencies, dead code, and missing tests, rank them by impact, and work through the list: refactoring, documenting, cleaning, and testing where it counts.
Scope the work →- Modernization targets ranked by agent impact
- Refactoring and documentation of core services
- Dependency cleanup and dead code removal
- Test coverage where it counts
- AI-legible codebase conventions
- Measured before and after velocity
AI-legible conventions
Naming, structure, and documentation standards that make the codebase easy for agents to navigate, and keep it that way as new code lands, so the cleanup does not decay back to baseline.
Book a scoping call →Measured velocity
We baseline engineer and agent performance before touching anything, then measure again after. The result is a number you can put in front of leadership, not a feeling that things got better.
Talk it through →For teams where the stack is the bottleneck.
If AI tooling underdelivers or velocity keeps dropping, the codebase is usually why.
Faster code is step one.
Here is what pairs with it.
Make the codebase an asset again.
Ranked targets, focused cleanup, measured results. Tell us about your stack and we will scope the first pass.
